DCF Node Config via Backbone and Trust Insertion
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Solution Overview
Problem
Configuring and maintaining data confidence fabrics (DCFs) with decentralized nodes poses challenges such as scalable configuration communication, authentication/authorization, trust insertion footprint size, and version management, particularly in large, geographically distributed environments with limited processing power and memory.
Innovation Solution
Implementing a DCF Backbone with a dynamic trust insertion API and config files that specify trust insertion technologies for each node, allowing for static and dynamic loading of trust components, and utilizing accelerator nodes for resource-constrained environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a DCF backbone with dynamic trust insertion API is implemented, then trust management flexibility and node configurability are improved, but system complexity and authentication/authorization overhead increase
Solution Approach 1:
The system segments trust management into modular components: a DCF backbone providing core functionality, dynamic trust insertion components for specific trust operations, and config files for node-specific configuration. This modular architecture allows flexible trust management while keeping each component's complexity manageable and independently configurable.
Solution Approach 2:
The patent introduces intermediary elements including a dynamic trust insertion API that mediates between trust requirements and implementation, config files that mediate between centralized policy and node execution, and accelerator nodes that mediate trust operations for resource-constrained devices. These intermediaries absorb complexity while providing flexible trust management.
2Manufacturing precision
If config files specify trust insertion technologies for each node, then node-specific trust configuration precision is improved, but configuration communication overhead and version management complexity increase
Solution Approach 1:
The system uses config files as lightweight copies of trust configuration data that can be distributed to nodes without requiring continuous communication. Each node receives a copy of its configuration specifications, enabling precise node-specific trust setup while minimizing ongoing communication overhead. Version updates are handled by distributing updated config file copies rather than continuous synchronization.
3Productivity
If accelerator nodes are used for resource-constrained environments, then trust insertion capability in limited processing/memory environments is improved, but device architecture complexity and coordination overhead increase
Solution Approach 1:
Accelerator nodes serve as intermediary specialized processors that handle trust insertion operations for resource-constrained devices. The accelerators absorb the computational complexity of trust operations, allowing resource-constrained nodes to maintain simple architectures while still achieving effective trust insertion through coordination with accelerator nodes.
Solution Approach 2:
The system segments trust processing functions between resource-constrained nodes (which handle data generation and basic operations) and accelerator nodes (which handle computationally intensive trust insertion operations). This functional segmentation allows each node type to be optimized for its specific role, improving overall trust insertion efficiency while keeping individual device architectures relatively simple.
Data Source
AI summary
One example method includes receiving, at a node of a data confidence fabric (DCF), a DCF backbone, installing the DCF backbone at the node, receiving a config file at the node, and the config file includes configuration information concerning the node, and receiving and installing a trust insertion component specified in the configuration information, where operation of the trust insertion component is enabled by the DCF backbone, and the trust insertion component is operable to associate trust metadata with data received by the node.